Products

Intelligence doesn’t emerge from examples alone. It’s shaped through practice, feedback, correction, and real constraints.

That’s how people learn, and it’s how AI systems improve.

We focus on what matters after the demo works. We design environments, feedback loops, and evaluation systems that shape model behavior in real workflows, on real data, with real stakes. Everything we build is meant to hold up in production, not just on benchmarks.

On the path to AGI, data quality, judgment, and evaluation matter more than model size.

RL Environments
Rubrics & Verifiers
SFT
RLHF
DPO
Human Evaluation
Expert Domains
Multimodal

RL Environments and Agent Training

Train agents on real workflows, not isolated prompts.

Instead of testing agents on single questions, we create environments that look like real work. For example, a coding agent fixing a bug across multiple files, running tests, handling failures, and retrying with context.
  • • Design task environments where agents plan, act, observe outcomes, and iterate
  • • Multi-step workflows like code refactors, tool usage, or research tasks
  • • Reward signals based on task completion, correctness, and behavior quality

Custom Rubrics and Verifiers

Make subjective judgment consistent and measurable.

When two engineers review the same output, they should reach the same conclusion. We design rubrics and verifiers that clearly define what counts as correct, acceptable, or wrong, whether it’s a code review, a summary, or an agent decision.
  • • Clear scoring criteria for correctness, reasoning, and instruction following
  • • Verifier rules to reduce reviewer subjectivity and drift
  • • Evaluation frameworks that can be audited and tied to SLAs

Supervised Fine Tuning

Show the model how the task should actually be done.

We create expert-written examples that demonstrate ideal behavior. For example, how a senior engineer would explain a code change, or how a support agent would handle a complex customer issue step by step.
  • • High-quality demonstrations written by domain experts
  • • Step-by-step reasoning and explanations where required
  • • Task-specific examples tailored to your product or workflow

RLHF and Preference Feedback

Teach models which outputs are actually better.

Instead of marking answers as simply right or wrong, experts compare multiple outputs and explain which one they would choose. For example, deciding which code solution is safer, more readable, or easier to maintain.
  • • Pairwise and ranked comparisons of model outputs
  • • Expert reviewers calibrated using shared evaluation rubrics
  • • Preference data suitable for reward model training and alignment

Direct Preference Optimization

Preference learning without complex reward models.

For teams using DPO, we generate clean chosen vs rejected examples. For instance, selecting a correct API implementation over a subtly broken one, with preferences grounded in expert judgment.
  • • Chosen and rejected response pairs aligned to target behavior
  • • Expert-reviewed preference signals with clear intent
  • • Datasets prepared specifically for DPO training pipelines

Human Evaluation

Human judgment where automation falls short.

Some things can’t be reliably scored by scripts. Humans review outputs for usefulness, clarity, and safety, such as whether a response would genuinely help a developer or confuse them.
  • • Expert review using clear, task-specific rubrics
  • • Consistency checks across multiple reviewers
  • • Validation of quality, safety, and edge-case behavior

Expert Professional Domains

Real expertise for high-stakes decisions.

When correctness matters, we bring in professionals who do this work every day. For example, senior engineers reviewing production code, or finance experts validating financial reasoning.
  • • Domain-specific data creation and review by vetted professionals
  • • Expert-aligned feedback for training and evaluation
  • • Support for regulated and accuracy-critical applications

Multimodal Data and Evaluation

Train and evaluate models across multiple input types.

Many real tasks combine text, visuals, and audio. We support multimodal workflows such as reviewing screenshots with explanations or evaluating video and audio outputs alongside text.
  • • Data creation and evaluation across text, images, audio, and video
  • • Cross-modal consistency and reasoning checks
  • • Support for multimodal agents and real-world applications